139 citations · 288 across the 5 of their papers we have counts for
8 papers
Ask Me Anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F. Chen +6
Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training.…
LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning
Neel Guha, Daniel E. Ho, Julian Nyarko +1
Can foundation models be guided to execute tasks involving legal reasoning? We believe that building a benchmark to answer this question will require sustained collaborative effort…
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
Lucia Zheng, Neel Guha, Brandon R. Anderson +2
While self-supervised learning has made rapid advances in natural language processing, it remains unclear when researchers should engage in resource-intensive domain-specific pretr…
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy
Amanda Coston, Neel Guha, Derek Ouyang +3
Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet litt…
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
Laurel Orr, Megan Leszczynski, Simran Arora +4
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the t…
Machine Learning for AC Optimal Power Flow
Neel Guha, Zhecheng Wang, Matt Wytock +1
We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and eng…